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两种机器学习混合模型用于预测2型糖尿病
Rahman Farnoosh1, Karlo Abnoosian1, Rasha Abbas Isewid1
1The School of Mathematics and Computer Science, Statistics, Iran University of Science and Technology, Tehran, Iran.
Journal of medical signals and sensors
|May 12, 2025
概括
这项研究引入了两种混合机器学习模型,用于准确诊断糖尿病. 这些模型有效处理异常值,改善糖尿病的早期检测和治疗.
科学领域:
- 医疗信息学 医疗信息学
- 计算生物学 计算生物学
- 医疗保健中的机器学习
背景情况:
- 全球糖尿病患病率不断上升,需要先进的诊断工具.
- 机器学习 (ML) 显示出疾病诊断的巨大潜力,包括糖尿病.
- 准确的糖尿病诊断对于及时干预和管理至关重要.
研究的目的:
- 开发和评估新的混合机器学习模型用于糖尿病诊断.
- 解决医疗数据集中异常数据的挑战,以提高诊断准确度.
- 将拟议的混合模型的性能与已建立的ML算法进行比较.
主要方法:
- 利用了来自伊拉克医院的1000个体检样本的数据集,分为糖尿病患者,非糖尿病患者和预测糖尿病患者.
- 开发了混合模型1 (K-medoids + Gaussian Naive Bayes与KDE) 来处理异常值.
- 开发了混合模型2 (K-means + Gaussian Naive Bayes与KDE),用于删除异常值的数据.
- 应用主要组件分析以减少维度和五倍交叉验证以进行评估.
主要成果:
- 与支持矢量机器,决策树 (J48) 和高斯天真贝叶斯相比,拟议的混合模型在糖尿病预测方面表现优越.
- 混合模型1在处理异常数据时实现了0.9743的平均准确性.
- 混合模型2实现了0.9867的平均准确度,在异常值被删除后.
- 精度,灵敏度和F1分数也被评估,这表明性能强.
结论:
- 这项研究成功地提出了糖尿病诊断的两个准确的混合模型.
- 这些模型有效地管理了异常数据,提高了诊断可靠性.
- 这些发现强调了ML在改善早期糖尿病诊断和治疗策略方面的潜力.
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